Kernel Methods: applications, promises and open issues
Speaker: Jean-Marc Mercier (Dr., Head of R&D of MPG Partners)
Date / Time: Tuesday, June 25, 2024 / 15:40
Place: Online Participation (please self-register first)
Abstract: In this seminar, we will present and illustrate a few fields of application of the kernel methods: partial differential equations, statistics, machine learning (supervised learning, clustering, generative methods, reinforcement learning). We will discuss the advantages, the problems encountered and the state of the art of these methods.
Additional Resources
- Presentation Slides
- Recorded Video (Raw)
- CodPy: a Python library for numerics, machine learning, and statistics
Biography
Jean-Marc Mercier is the head of R&D of MPG-Partners, a French, consulting firm operating in the industrial finance sector, specialized in risk management. Dr. Mercier is a mathematician, software developer, business analyst consultant, having more than 20 years R&D experience as quantitative analyst. He has a PhD in Applied Mathematics from Bordeaux University. Having his PhD, Jean-Marc started a public researcher carrier (European Research Program), before turning to private R&D, mathematical finance. Dr. Mercier also started in 2005 a research program of his own, concerning the curse of dimensionality. This led him to develop a framework using Reproducing Kernel Hilbert Space Methods, which is used today in his company as the foundation of several applications in mathematical finance.